Description
Book SynopsisFinancial hedging refers to taking out investments in order to reduce or cancel the risk in another investment. Its purpose is to minimise unwanted business risk while still allowing the business to profit from investment activity. The problem of credit risk is one of the most important problems in finance. It consists of computing the probability of a firm defaulting on a debt. The time evolution of rating for credit risk models can be studied by means of Markov transition models. This book looks at the homogeneous and non-homogeneous semi-Markov backward credit risk migration models. A joint optimisation model for a firm''s hedging and leverage decisions is also examined to help establish an integrated framework for value creation. Rather than artificially separating the two interrelated parts of the firm''s financial policy, both corporate decision variables are treated as endogenous. Furthermore, the cross-sectional variation in indirect bankruptcy costs is discussed, possibly resulting from a deterioration of relationships with customers, suppliers or other stakeholders prior to the legal act of bankruptcy. The effect of probability weighting on hedging decisions is explored in this book. Observed hedge ratios in a storage context are close to zero in many situations and often smaller than the standard minimum-variance hedge zero. Thus, the importance of probability weighting in decision making and how it can cause dramatic changes in behavior is looked at. This book also re-examines hedging performance of the minimum variance hedge ratios (MVHR) estimated using both the OLS and the GARCH-type models with S&P 500 index futures contracts. In particular, the out-of-sample comparison of hedging performance of the MVHRs under different market volatility regimes are looked at. In addition, the analysis for parametric and non-parametric Markov processes are discussed and the construction of the transition matrix in these two different cases. Several possible strategies where the investors recalibrate their portfolios at a fixed temporal horizon are proposed. The authors also show how the Markov assumption can be used to forecast the portfolio returns and some simple empirical comparisons between Markovian strategies and classic reward-risk ones. Finally, articles in this book contribute to the literature on futures hedging in commodity futures markets by using wavelet transform analysis to define an explicit and tractable concept of time horizon. Differences in hedge ratios are discussed both across commodities and, for each commodity, over all time horizons of decision-making.
